Measuring Critical Thinking in Science: Systematic Review
Bibliographic record
Abstract
The review aims to explore possible methods for critical thinking assessment in science from previous studies. For a long time, critical thinking has been among most talked topics among researchers and academicians, due to its nature in improving one’s quality of life such as becoming an effective problem solver and logical thinkers. In this study, literature search for related studies was conducted through online databases, The Educational Resource Information Centre (ERIC) dated from the year 2010 till 2017 using keywords such as critical thinking, science, science education and measurement. Only refereed/peer-reviewed journals that fulfilled criteria needed were selected for the study with the findings from web-based service providers, including Sage Journals, Springer, Taylor & Francis, Science Direct, and Wiley Online Library. The findings were analyzed using document analysis technique to answer research questions of this study. This systematic review reveals that critical thinking can be assessed using quantitative or qualitative methods depending on the scope and dimensions of the research. Although there are studies on critical thinking in science, the assessment tools used are instrumented for critical thinking in general setting which focuses in general context. However, when it comes to assessing critical thinking in science secondary school/high school, the findings were limited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.040 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".